Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations140486
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.5 MiB
Average record size in memory168.0 B

Variable types

Numeric8
Text4
Categorical6
Boolean2

Alerts

Academic Pressure is highly overall correlated with Age and 6 other fieldsHigh correlation
Age is highly overall correlated with Academic Pressure and 4 other fieldsHigh correlation
CGPA is highly overall correlated with Academic Pressure and 6 other fieldsHigh correlation
Depression is highly overall correlated with Academic Pressure and 6 other fieldsHigh correlation
Job Satisfaction is highly overall correlated with Academic Pressure and 4 other fieldsHigh correlation
Study Satisfaction is highly overall correlated with Academic Pressure and 6 other fieldsHigh correlation
Work Pressure is highly overall correlated with Academic Pressure and 4 other fieldsHigh correlation
Working Professional or Student is highly overall correlated with Academic Pressure and 6 other fieldsHigh correlation
Sleep Duration is highly imbalanced (61.2%) Imbalance
Dietary Habits is highly imbalanced (64.9%) Imbalance
id is uniformly distributed Uniform
id has unique values Unique
Academic Pressure has 112604 (80.2%) zeros Zeros
Work Pressure has 27882 (19.8%) zeros Zeros
Study Satisfaction has 112604 (80.2%) zeros Zeros
Job Satisfaction has 27882 (19.8%) zeros Zeros
Work/Study Hours has 12042 (8.6%) zeros Zeros

Reproduction

Analysis started2024-12-15 16:35:40.670543
Analysis finished2024-12-15 16:35:49.709102
Duration9.04 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Uniform  Unique 

Distinct140486
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70342.754
Minimum0
Maximum140699
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-15T23:35:49.758449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7032.25
Q135164.25
median70343.5
Q3105519.75
95-th percentile133659.75
Maximum140699
Range140699
Interquartile range (IQR)70355.5

Descriptive statistics

Standard deviation40617.763
Coefficient of variation (CV)0.57742639
Kurtosis-1.200123
Mean70342.754
Median Absolute Deviation (MAD)35178
Skewness0.0001064067
Sum9.8821721 × 109
Variance1.6498026 × 109
MonotonicityStrictly increasing
2024-12-15T23:35:49.839670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
93796 1
 
< 0.1%
93790 1
 
< 0.1%
93791 1
 
< 0.1%
93792 1
 
< 0.1%
93793 1
 
< 0.1%
93794 1
 
< 0.1%
93795 1
 
< 0.1%
93797 1
 
< 0.1%
93771 1
 
< 0.1%
Other values (140476) 140476
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
140699 1
< 0.1%
140698 1
< 0.1%
140697 1
< 0.1%
140696 1
< 0.1%
140695 1
< 0.1%
140694 1
< 0.1%
140693 1
< 0.1%
140692 1
< 0.1%
140691 1
< 0.1%
140690 1
< 0.1%

Name
Text

Distinct422
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-12-15T23:35:50.101195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length5.6446265
Min length2

Characters and Unicode

Total characters792991
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)0.1%

Sample

1st rowAaradhya
2nd rowVivan
3rd rowYuvraj
4th rowYuvraj
5th rowRhea
ValueCountFrequency (%)
rohan 3177
 
2.3%
aarav 2333
 
1.7%
rupak 2176
 
1.5%
aaradhya 2041
 
1.5%
anvi 2032
 
1.4%
raghavendra 1875
 
1.3%
vani 1656
 
1.2%
tushar 1595
 
1.1%
ritvik 1589
 
1.1%
shiv 1566
 
1.1%
Other values (413) 120448
85.7%
2024-12-15T23:35:50.445062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 162123
20.4%
i 82287
 
10.4%
h 73737
 
9.3%
n 53900
 
6.8%
r 48397
 
6.1%
s 35419
 
4.5%
A 33379
 
4.2%
v 31490
 
4.0%
R 23709
 
3.0%
y 22328
 
2.8%
Other values (41) 226222
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 792991
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 162123
20.4%
i 82287
 
10.4%
h 73737
 
9.3%
n 53900
 
6.8%
r 48397
 
6.1%
s 35419
 
4.5%
A 33379
 
4.2%
v 31490
 
4.0%
R 23709
 
3.0%
y 22328
 
2.8%
Other values (41) 226222
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 792991
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 162123
20.4%
i 82287
 
10.4%
h 73737
 
9.3%
n 53900
 
6.8%
r 48397
 
6.1%
s 35419
 
4.5%
A 33379
 
4.2%
v 31490
 
4.0%
R 23709
 
3.0%
y 22328
 
2.8%
Other values (41) 226222
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 792991
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 162123
20.4%
i 82287
 
10.4%
h 73737
 
9.3%
n 53900
 
6.8%
r 48397
 
6.1%
s 35419
 
4.5%
A 33379
 
4.2%
v 31490
 
4.0%
R 23709
 
3.0%
y 22328
 
2.8%
Other values (41) 226222
28.5%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Male
77350 
Female
63136 

Length

Max length6
Median length4
Mean length4.8988227
Min length4

Characters and Unicode

Total characters688216
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 77350
55.1%
Female 63136
44.9%

Length

2024-12-15T23:35:50.543362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-15T23:35:50.610004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 77350
55.1%
female 63136
44.9%

Most occurring characters

ValueCountFrequency (%)
e 203622
29.6%
a 140486
20.4%
l 140486
20.4%
M 77350
 
11.2%
F 63136
 
9.2%
m 63136
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 688216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 203622
29.6%
a 140486
20.4%
l 140486
20.4%
M 77350
 
11.2%
F 63136
 
9.2%
m 63136
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 688216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 203622
29.6%
a 140486
20.4%
l 140486
20.4%
M 77350
 
11.2%
F 63136
 
9.2%
m 63136
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 688216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 203622
29.6%
a 140486
20.4%
l 140486
20.4%
M 77350
 
11.2%
F 63136
 
9.2%
m 63136
 
9.2%

Age
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.401342
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-15T23:35:50.676361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q130
median42
Q351
95-th percentile58
Maximum60
Range42
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.379656
Coefficient of variation (CV)0.30641696
Kurtosis-1.1481766
Mean40.401342
Median Absolute Deviation (MAD)10
Skewness-0.21909687
Sum5675823
Variance153.25589
MonotonicityNot monotonic
2024-12-15T23:35:50.794664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
56 5241
 
3.7%
49 5094
 
3.6%
38 4557
 
3.2%
53 4523
 
3.2%
57 4392
 
3.1%
47 4194
 
3.0%
46 4078
 
2.9%
51 3926
 
2.8%
54 3924
 
2.8%
18 3898
 
2.8%
Other values (33) 96659
68.8%
ValueCountFrequency (%)
18 3898
2.8%
19 2622
1.9%
20 3499
2.5%
21 2732
1.9%
22 2063
1.5%
23 2890
2.1%
24 3345
2.4%
25 2923
2.1%
26 2104
1.5%
27 2609
1.9%
ValueCountFrequency (%)
60 2497
1.8%
59 3778
2.7%
58 2932
2.1%
57 4392
3.1%
56 5241
3.7%
55 2846
2.0%
54 3924
2.8%
53 4523
3.2%
52 2589
1.8%
51 3926
2.8%

City
Text

Distinct98
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-12-15T23:35:50.923058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length18
Median length11
Mean length7.0176317
Min length2

Characters and Unicode

Total characters985879
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)< 0.1%

Sample

1st rowLudhiana
2nd rowVaranasi
3rd rowVisakhapatnam
4th rowMumbai
5th rowKanpur
ValueCountFrequency (%)
kalyan 6587
 
4.7%
patna 5918
 
4.2%
vasai-virar 5756
 
4.1%
kolkata 5683
 
4.0%
ahmedabad 5601
 
4.0%
meerut 5520
 
3.9%
ludhiana 5216
 
3.7%
pune 5200
 
3.7%
rajkot 5193
 
3.7%
visakhapatnam 5173
 
3.7%
Other values (89) 84643
60.2%
2024-12-15T23:35:51.251324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 218818
22.2%
r 75317
 
7.6%
n 67805
 
6.9%
i 58547
 
5.9%
d 49524
 
5.0%
e 47235
 
4.8%
u 42732
 
4.3%
h 38177
 
3.9%
o 32171
 
3.3%
t 32132
 
3.3%
Other values (39) 323421
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 985879
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 218818
22.2%
r 75317
 
7.6%
n 67805
 
6.9%
i 58547
 
5.9%
d 49524
 
5.0%
e 47235
 
4.8%
u 42732
 
4.3%
h 38177
 
3.9%
o 32171
 
3.3%
t 32132
 
3.3%
Other values (39) 323421
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 985879
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 218818
22.2%
r 75317
 
7.6%
n 67805
 
6.9%
i 58547
 
5.9%
d 49524
 
5.0%
e 47235
 
4.8%
u 42732
 
4.3%
h 38177
 
3.9%
o 32171
 
3.3%
t 32132
 
3.3%
Other values (39) 323421
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 985879
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 218818
22.2%
r 75317
 
7.6%
n 67805
 
6.9%
i 58547
 
5.9%
d 49524
 
5.0%
e 47235
 
4.8%
u 42732
 
4.3%
h 38177
 
3.9%
o 32171
 
3.3%
t 32132
 
3.3%
Other values (39) 323421
32.8%

Working Professional or Student
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Working Professional
112604 
Student
27882 

Length

Max length20
Median length20
Mean length17.419914
Min length7

Characters and Unicode

Total characters2447254
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWorking Professional
2nd rowWorking Professional
3rd rowStudent
4th rowWorking Professional
5th rowWorking Professional

Common Values

ValueCountFrequency (%)
Working Professional 112604
80.2%
Student 27882
 
19.8%

Length

2024-12-15T23:35:51.346643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-15T23:35:51.408037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
working 112604
44.5%
professional 112604
44.5%
student 27882
 
11.0%

Most occurring characters

ValueCountFrequency (%)
o 337812
13.8%
n 253090
10.3%
r 225208
 
9.2%
i 225208
 
9.2%
s 225208
 
9.2%
e 140486
 
5.7%
W 112604
 
4.6%
l 112604
 
4.6%
a 112604
 
4.6%
f 112604
 
4.6%
Other values (8) 589826
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2447254
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 337812
13.8%
n 253090
10.3%
r 225208
 
9.2%
i 225208
 
9.2%
s 225208
 
9.2%
e 140486
 
5.7%
W 112604
 
4.6%
l 112604
 
4.6%
a 112604
 
4.6%
f 112604
 
4.6%
Other values (8) 589826
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2447254
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 337812
13.8%
n 253090
10.3%
r 225208
 
9.2%
i 225208
 
9.2%
s 225208
 
9.2%
e 140486
 
5.7%
W 112604
 
4.6%
l 112604
 
4.6%
a 112604
 
4.6%
f 112604
 
4.6%
Other values (8) 589826
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2447254
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 337812
13.8%
n 253090
10.3%
r 225208
 
9.2%
i 225208
 
9.2%
s 225208
 
9.2%
e 140486
 
5.7%
W 112604
 
4.6%
l 112604
 
4.6%
a 112604
 
4.6%
f 112604
 
4.6%
Other values (8) 589826
24.1%
Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-12-15T23:35:51.670372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length9.6089575
Min length2

Characters and Unicode

Total characters1349924
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowChef
2nd rowTeacher
3rd rowStudent
4th rowTeacher
5th rowBusiness Analyst
ValueCountFrequency (%)
student 27886
 
15.5%
teacher 24900
 
13.8%
consultant 8940
 
5.0%
other 8572
 
4.7%
content 7811
 
4.3%
writer 7811
 
4.3%
manager 7735
 
4.3%
analyst 6755
 
3.7%
architect 4362
 
2.4%
engineer 4154
 
2.3%
Other values (64) 71552
39.6%
2024-12-15T23:35:51.904751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 181143
13.4%
t 163272
12.1%
n 116874
 
8.7%
r 108653
 
8.0%
a 103366
 
7.7%
c 63038
 
4.7%
i 59219
 
4.4%
u 56455
 
4.2%
h 52915
 
3.9%
s 44257
 
3.3%
Other values (40) 400732
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1349924
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 181143
13.4%
t 163272
12.1%
n 116874
 
8.7%
r 108653
 
8.0%
a 103366
 
7.7%
c 63038
 
4.7%
i 59219
 
4.4%
u 56455
 
4.2%
h 52915
 
3.9%
s 44257
 
3.3%
Other values (40) 400732
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1349924
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 181143
13.4%
t 163272
12.1%
n 116874
 
8.7%
r 108653
 
8.0%
a 103366
 
7.7%
c 63038
 
4.7%
i 59219
 
4.4%
u 56455
 
4.2%
h 52915
 
3.9%
s 44257
 
3.3%
Other values (40) 400732
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1349924
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 181143
13.4%
t 163272
12.1%
n 116874
 
8.7%
r 108653
 
8.0%
a 103366
 
7.7%
c 63038
 
4.7%
i 59219
 
4.4%
u 56455
 
4.2%
h 52915
 
3.9%
s 44257
 
3.3%
Other values (40) 400732
29.7%

Academic Pressure
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6236351
Minimum0
Maximum5
Zeros112604
Zeros (%)80.2%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-15T23:35:51.977551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3960374
Coefficient of variation (CV)2.2385485
Kurtosis3.0875179
Mean0.6236351
Median Absolute Deviation (MAD)0
Skewness2.1186444
Sum87612
Variance1.9489204
MonotonicityNot monotonic
2024-12-15T23:35:52.035028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 112604
80.2%
3 7460
 
5.3%
5 6293
 
4.5%
4 5154
 
3.7%
1 4799
 
3.4%
2 4176
 
3.0%
ValueCountFrequency (%)
0 112604
80.2%
1 4799
 
3.4%
2 4176
 
3.0%
3 7460
 
5.3%
4 5154
 
3.7%
5 6293
 
4.5%
ValueCountFrequency (%)
5 6293
 
4.5%
4 5154
 
3.7%
3 7460
 
5.3%
2 4176
 
3.0%
1 4799
 
3.4%
0 112604
80.2%

Work Pressure
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4034993
Minimum0
Maximum5
Zeros27882
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-15T23:35:52.131878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7361274
Coefficient of variation (CV)0.72233324
Kurtosis-1.2911811
Mean2.4034993
Median Absolute Deviation (MAD)2
Skewness0.054049958
Sum337658
Variance3.0141384
MonotonicityNot monotonic
2024-12-15T23:35:52.197882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 27882
19.8%
2 24342
17.3%
4 22476
16.0%
5 22387
15.9%
3 21868
15.6%
1 21531
15.3%
ValueCountFrequency (%)
0 27882
19.8%
1 21531
15.3%
2 24342
17.3%
3 21868
15.6%
4 22476
16.0%
5 22387
15.9%
ValueCountFrequency (%)
5 22387
15.9%
4 22476
16.0%
3 21868
15.6%
2 24342
17.3%
1 21531
15.3%
0 27882
19.8%

CGPA
Real number (ℝ)

High correlation 

Distinct332
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71850877
Minimum-1
Maximum10
Zeros0
Zeros (%)0.0%
Negative112604
Negative (%)80.2%
Memory size2.1 MiB
2024-12-15T23:35:52.280307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile8.91
Maximum10
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.5146505
Coefficient of variation (CV)4.8915903
Kurtosis0.87210322
Mean0.71850877
Median Absolute Deviation (MAD)0
Skewness1.6376282
Sum100940.42
Variance12.352768
MonotonicityNot monotonic
2024-12-15T23:35:52.363707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 112604
80.2%
8.04 821
 
0.6%
9.96 425
 
0.3%
5.74 409
 
0.3%
8.95 371
 
0.3%
9.21 343
 
0.2%
7.25 339
 
0.2%
7.09 320
 
0.2%
7.88 318
 
0.2%
9.44 317
 
0.2%
Other values (322) 24219
 
17.2%
ValueCountFrequency (%)
-1 112604
80.2%
5.03 17
 
< 0.1%
5.06 15
 
< 0.1%
5.08 95
 
0.1%
5.09 20
 
< 0.1%
5.1 66
 
< 0.1%
5.11 112
 
0.1%
5.12 167
 
0.1%
5.14 19
 
< 0.1%
5.16 209
 
0.1%
ValueCountFrequency (%)
10 58
 
< 0.1%
9.98 59
 
< 0.1%
9.97 139
 
0.1%
9.96 425
0.3%
9.95 133
 
0.1%
9.94 71
 
0.1%
9.93 274
0.2%
9.92 60
 
< 0.1%
9.91 123
 
0.1%
9.9 28
 
< 0.1%

Study Satisfaction
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58447817
Minimum0
Maximum5
Zeros112604
Zeros (%)80.2%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-15T23:35:52.429839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3216756
Coefficient of variation (CV)2.2612917
Kurtosis3.4859105
Mean0.58447817
Median Absolute Deviation (MAD)0
Skewness2.1900604
Sum82111
Variance1.7468264
MonotonicityNot monotonic
2024-12-15T23:35:52.496588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 112604
80.2%
4 6358
 
4.5%
2 5837
 
4.2%
3 5819
 
4.1%
1 5448
 
3.9%
5 4420
 
3.1%
ValueCountFrequency (%)
0 112604
80.2%
1 5448
 
3.9%
2 5837
 
4.2%
3 5819
 
4.1%
4 6358
 
4.5%
5 4420
 
3.1%
ValueCountFrequency (%)
5 4420
 
3.1%
4 6358
 
4.5%
3 5819
 
4.1%
2 5837
 
4.2%
1 5448
 
3.9%
0 112604
80.2%

Job Satisfaction
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3842589
Minimum0
Maximum5
Zeros27882
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-15T23:35:52.551100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7363003
Coefficient of variation (CV)0.72823477
Kurtosis-1.2807964
Mean2.3842589
Median Absolute Deviation (MAD)2
Skewness0.084897153
Sum334955
Variance3.0147386
MonotonicityNot monotonic
2024-12-15T23:35:52.610360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 27882
19.8%
2 24733
17.6%
5 22775
16.2%
1 22282
15.9%
3 21924
15.6%
4 20890
14.9%
ValueCountFrequency (%)
0 27882
19.8%
1 22282
15.9%
2 24733
17.6%
3 21924
15.6%
4 20890
14.9%
5 22775
16.2%
ValueCountFrequency (%)
5 22775
16.2%
4 20890
14.9%
3 21924
15.6%
2 24733
17.6%
1 22282
15.9%
0 27882
19.8%

Sleep Duration
Categorical

Imbalance 

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Less than 5 hours
38723 
7-8 hours
36917 
More than 8 hours
32679 
5-6 hours
32088 
3-4 hours
 
12
Other values (31)
 
67

Length

Max length17
Median length17
Mean length13.065822
Min length2

Characters and Unicode

Total characters1835565
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowMore than 8 hours
2nd rowLess than 5 hours
3rd row5-6 hours
4th rowLess than 5 hours
5th row5-6 hours

Common Values

ValueCountFrequency (%)
Less than 5 hours 38723
27.6%
7-8 hours 36917
26.3%
More than 8 hours 32679
23.3%
5-6 hours 32088
22.8%
3-4 hours 12
 
< 0.1%
6-7 hours 8
 
< 0.1%
4-5 hours 7
 
< 0.1%
2-3 hours 5
 
< 0.1%
4-6 hours 5
 
< 0.1%
6-8 hours 4
 
< 0.1%
Other values (26) 38
 
< 0.1%

Length

2024-12-15T23:35:52.686954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hours 140471
33.1%
than 71403
16.8%
5 38724
 
9.1%
less 38723
 
9.1%
7-8 36917
 
8.7%
8 32680
 
7.7%
more 32679
 
7.7%
5-6 32088
 
7.6%
3-4 12
 
< 0.1%
6-7 8
 
< 0.1%
Other values (27) 57
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
283276
15.4%
s 217918
11.9%
h 211878
11.5%
o 173160
9.4%
r 173156
9.4%
u 140476
7.7%
e 71412
 
3.9%
t 71409
 
3.9%
n 71409
 
3.9%
a 71408
 
3.9%
Other values (28) 350063
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1835565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
283276
15.4%
s 217918
11.9%
h 211878
11.5%
o 173160
9.4%
r 173156
9.4%
u 140476
7.7%
e 71412
 
3.9%
t 71409
 
3.9%
n 71409
 
3.9%
a 71408
 
3.9%
Other values (28) 350063
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1835565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
283276
15.4%
s 217918
11.9%
h 211878
11.5%
o 173160
9.4%
r 173156
9.4%
u 140476
7.7%
e 71412
 
3.9%
t 71409
 
3.9%
n 71409
 
3.9%
a 71408
 
3.9%
Other values (28) 350063
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1835565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
283276
15.4%
s 217918
11.9%
h 211878
11.5%
o 173160
9.4%
r 173156
9.4%
u 140476
7.7%
e 71412
 
3.9%
t 71409
 
3.9%
n 71409
 
3.9%
a 71408
 
3.9%
Other values (28) 350063
19.1%

Dietary Habits
Categorical

Imbalance 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Moderate
49628 
Unhealthy
46147 
Healthy
44688 
Yes
 
2
No
 
2
Other values (18)
 
19

Length

Max length17
Median length12
Mean length8.010122
Min length1

Characters and Unicode

Total characters1125310
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowHealthy
2nd rowUnhealthy
3rd rowHealthy
4th rowModerate
5th rowUnhealthy

Common Values

ValueCountFrequency (%)
Moderate 49628
35.3%
Unhealthy 46147
32.8%
Healthy 44688
31.8%
Yes 2
 
< 0.1%
No 2
 
< 0.1%
More Healthy 2
 
< 0.1%
Electrician 1
 
< 0.1%
Pratham 1
 
< 0.1%
BSc 1
 
< 0.1%
Gender 1
 
< 0.1%
Other values (13) 13
 
< 0.1%

Length

2024-12-15T23:35:52.759169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
moderate 49628
35.3%
unhealthy 46147
32.8%
healthy 44693
31.8%
no 3
 
< 0.1%
more 2
 
< 0.1%
less 2
 
< 0.1%
yes 2
 
< 0.1%
electrician 1
 
< 0.1%
m.tech 1
 
< 0.1%
12 1
 
< 0.1%
Other values (13) 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 190108
16.9%
a 140476
12.5%
t 140471
12.5%
h 136991
12.2%
l 90844
8.1%
y 90840
8.1%
o 49637
 
4.4%
r 49636
 
4.4%
M 49633
 
4.4%
d 49630
 
4.4%
Other values (26) 137044
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1125310
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 190108
16.9%
a 140476
12.5%
t 140471
12.5%
h 136991
12.2%
l 90844
8.1%
y 90840
8.1%
o 49637
 
4.4%
r 49636
 
4.4%
M 49633
 
4.4%
d 49630
 
4.4%
Other values (26) 137044
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1125310
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 190108
16.9%
a 140476
12.5%
t 140471
12.5%
h 136991
12.2%
l 90844
8.1%
y 90840
8.1%
o 49637
 
4.4%
r 49636
 
4.4%
M 49633
 
4.4%
d 49630
 
4.4%
Other values (26) 137044
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1125310
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 190108
16.9%
a 140476
12.5%
t 140471
12.5%
h 136991
12.2%
l 90844
8.1%
y 90840
8.1%
o 49637
 
4.4%
r 49636
 
4.4%
M 49633
 
4.4%
d 49630
 
4.4%
Other values (26) 137044
12.2%

Degree
Text

Distinct112
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-12-15T23:35:52.972777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length4.3828068
Min length1

Characters and Unicode

Total characters615723
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)< 0.1%

Sample

1st rowBHM
2nd rowLLB
3rd rowB.Pharm
4th rowBBA
5th rowBBA
ValueCountFrequency (%)
class 14702
 
9.5%
12 14701
 
9.5%
b.ed 11680
 
7.5%
b.arch 8729
 
5.6%
b.com 8107
 
5.2%
b.pharm 5851
 
3.8%
bca 5728
 
3.7%
m.ed 5662
 
3.6%
mca 5225
 
3.4%
bba 5026
 
3.2%
Other values (105) 69791
45.0%
2024-12-15T23:35:53.231183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 85058
13.8%
M 60141
 
9.8%
. 56567
 
9.2%
C 36857
 
6.0%
A 35168
 
5.7%
h 31136
 
5.1%
s 29422
 
4.8%
c 27532
 
4.5%
a 25129
 
4.1%
E 24075
 
3.9%
Other values (46) 204638
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 615723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 85058
13.8%
M 60141
 
9.8%
. 56567
 
9.2%
C 36857
 
6.0%
A 35168
 
5.7%
h 31136
 
5.1%
s 29422
 
4.8%
c 27532
 
4.5%
a 25129
 
4.1%
E 24075
 
3.9%
Other values (46) 204638
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 615723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 85058
13.8%
M 60141
 
9.8%
. 56567
 
9.2%
C 36857
 
6.0%
A 35168
 
5.7%
h 31136
 
5.1%
s 29422
 
4.8%
c 27532
 
4.5%
a 25129
 
4.1%
E 24075
 
3.9%
Other values (46) 204638
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 615723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 85058
13.8%
M 60141
 
9.8%
. 56567
 
9.2%
C 36857
 
6.0%
A 35168
 
5.7%
h 31136
 
5.1%
s 29422
 
4.8%
c 27532
 
4.5%
a 25129
 
4.1%
E 24075
 
3.9%
Other values (46) 204638
33.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
71057 
True
69429 
ValueCountFrequency (%)
False 71057
50.6%
True 69429
49.4%
2024-12-15T23:35:53.306862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Work/Study Hours
Real number (ℝ)

Zeros 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2520038
Minimum0
Maximum12
Zeros12042
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-15T23:35:53.351717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q310
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.8532697
Coefficient of variation (CV)0.61632555
Kurtosis-1.2833661
Mean6.2520038
Median Absolute Deviation (MAD)4
Skewness-0.12786456
Sum878319
Variance14.847687
MonotonicityNot monotonic
2024-12-15T23:35:53.403817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10 14172
10.1%
11 12807
9.1%
9 12691
9.0%
0 12042
8.6%
12 11386
8.1%
2 10586
 
7.5%
6 10420
 
7.4%
7 9856
 
7.0%
1 9789
 
7.0%
3 9463
 
6.7%
Other values (3) 27274
19.4%
ValueCountFrequency (%)
0 12042
8.6%
1 9789
7.0%
2 10586
7.5%
3 9463
6.7%
4 9052
6.4%
5 9325
6.6%
6 10420
7.4%
7 9856
7.0%
8 8897
6.3%
9 12691
9.0%
ValueCountFrequency (%)
12 11386
8.1%
11 12807
9.1%
10 14172
10.1%
9 12691
9.0%
8 8897
6.3%
7 9856
7.0%
6 10420
7.4%
5 9325
6.6%
4 9052
6.4%
3 9463
6.7%

Financial Stress
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2.0
31423 
5.0
28221 
4.0
27724 
1.0
27171 
3.0
25947 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters421458
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
2.0 31423
22.4%
5.0 28221
20.1%
4.0 27724
19.7%
1.0 27171
19.3%
3.0 25947
18.5%

Length

2024-12-15T23:35:53.456411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-15T23:35:53.511371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 31423
22.4%
5.0 28221
20.1%
4.0 27724
19.7%
1.0 27171
19.3%
3.0 25947
18.5%

Most occurring characters

ValueCountFrequency (%)
. 140486
33.3%
0 140486
33.3%
2 31423
 
7.5%
5 28221
 
6.7%
4 27724
 
6.6%
1 27171
 
6.4%
3 25947
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 421458
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 140486
33.3%
0 140486
33.3%
2 31423
 
7.5%
5 28221
 
6.7%
4 27724
 
6.6%
1 27171
 
6.4%
3 25947
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 421458
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 140486
33.3%
0 140486
33.3%
2 31423
 
7.5%
5 28221
 
6.7%
4 27724
 
6.6%
1 27171
 
6.4%
3 25947
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 421458
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 140486
33.3%
0 140486
33.3%
2 31423
 
7.5%
5 28221
 
6.7%
4 27724
 
6.6%
1 27171
 
6.4%
3 25947
 
6.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
70642 
True
69844 
ValueCountFrequency (%)
False 70642
50.3%
True 69844
49.7%
2024-12-15T23:35:53.571939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Depression
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
115007 
1
25479 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters140486
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 115007
81.9%
1 25479
 
18.1%

Length

2024-12-15T23:35:53.628148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-15T23:35:53.681475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 115007
81.9%
1 25479
 
18.1%

Most occurring characters

ValueCountFrequency (%)
0 115007
81.9%
1 25479
 
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140486
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 115007
81.9%
1 25479
 
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140486
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 115007
81.9%
1 25479
 
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140486
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 115007
81.9%
1 25479
 
18.1%

Interactions

2024-12-15T23:35:48.026318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:43.920682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:44.767098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:45.444723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:45.917213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.444180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.900538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:47.557727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:48.145536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:44.044218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:44.829428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:45.504792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.010424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.496200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.962863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:47.631998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:48.264449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:44.153297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:44.892982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:45.558509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.089558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.554089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:47.013354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:47.692415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:48.443406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:44.258451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:44.953739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:45.611949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.147061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.608663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:47.073669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:47.747268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:48.520198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:44.353326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:45.010263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:45.678410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.210997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.666195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:47.140832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:47.800049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:48.591159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:44.549118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:45.265762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:45.732174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.273618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.720201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:47.200520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:47.853372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:48.661191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:44.618254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:45.319955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:45.798649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.328661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.776947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:47.262088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:47.907081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:48.751988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:44.683619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:45.384830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:45.860049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.385488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:46.840855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:47.341228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-15T23:35:47.958807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-15T23:35:53.728847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Academic PressureAgeCGPADepressionDietary HabitsFamily History of Mental IllnessFinancial StressGenderHave you ever had suicidal thoughts ?Job SatisfactionSleep DurationStudy SatisfactionWork PressureWork/Study HoursWorking Professional or Studentid
Academic Pressure1.000-0.5630.9840.5890.0340.0180.0490.0110.180-0.6960.0200.983-0.6950.1211.0000.001
Age-0.5631.000-0.5610.6240.0300.0140.0570.0520.1830.4130.025-0.5610.343-0.1140.7000.002
CGPA0.984-0.5611.0000.5230.0280.0120.0330.0220.138-0.6950.0190.983-0.6950.1161.0000.001
Depression0.5890.6240.5231.0000.1520.0160.2340.0080.3490.5350.0920.5310.5420.2070.5220.003
Dietary Habits0.0340.0300.0280.1521.0000.0000.0270.0380.0660.0270.0000.0280.0280.0120.0540.000
Family History of Mental Illness0.0180.0140.0120.0160.0001.0000.0130.0150.0090.0170.0030.0130.0150.0150.0130.003
Financial Stress0.0490.0570.0330.2340.0270.0131.0000.0110.0920.0410.0200.0360.0350.0240.0640.000
Gender0.0110.0520.0220.0080.0380.0150.0111.0000.0100.0150.0030.0130.0050.0150.0060.006
Have you ever had suicidal thoughts ?0.1800.1830.1380.3490.0660.0090.0920.0101.0000.1470.0360.1430.1460.0680.1380.000
Job Satisfaction-0.6960.413-0.6950.5350.0270.0170.0410.0150.1471.0000.014-0.6950.472-0.1011.0000.001
Sleep Duration0.0200.0250.0190.0920.0000.0030.0200.0030.0360.0141.0000.0160.0160.0130.0290.000
Study Satisfaction0.983-0.5610.9830.5310.0280.0130.0360.0130.143-0.6950.0161.000-0.6950.1141.0000.002
Work Pressure-0.6950.343-0.6950.5420.0280.0150.0350.0050.1460.4720.016-0.6951.000-0.0831.0000.001
Work/Study Hours0.121-0.1140.1160.2070.0120.0150.0240.0150.068-0.1010.0130.114-0.0831.0000.1410.002
Working Professional or Student1.0000.7001.0000.5220.0540.0130.0640.0060.1381.0000.0291.0001.0000.1411.0000.000
id0.0010.0020.0010.0030.0000.0030.0000.0060.0000.0010.0000.0020.0010.0020.0001.000

Missing values

2024-12-15T23:35:48.907272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-15T23:35:49.253253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idNameGenderAgeCityWorking Professional or StudentProfessionAcademic PressureWork PressureCGPAStudy SatisfactionJob SatisfactionSleep DurationDietary HabitsDegreeHave you ever had suicidal thoughts ?Work/Study HoursFinancial StressFamily History of Mental IllnessDepression
00AaradhyaFemale49.0LudhianaWorking ProfessionalChef0.05.0-1.000.02.0More than 8 hoursHealthyBHMNo1.02.0No0
11VivanMale26.0VaranasiWorking ProfessionalTeacher0.04.0-1.000.03.0Less than 5 hoursUnhealthyLLBYes7.03.0No1
22YuvrajMale33.0VisakhapatnamStudentStudent5.00.08.972.00.05-6 hoursHealthyB.PharmYes3.01.0No1
33YuvrajMale22.0MumbaiWorking ProfessionalTeacher0.05.0-1.000.01.0Less than 5 hoursModerateBBAYes10.01.0Yes1
44RheaFemale30.0KanpurWorking ProfessionalBusiness Analyst0.01.0-1.000.01.05-6 hoursUnhealthyBBAYes9.04.0Yes0
55VaniFemale59.0AhmedabadWorking ProfessionalFinanancial Analyst0.02.0-1.000.05.05-6 hoursHealthyMCANo7.05.0No0
66RitvikMale47.0ThaneWorking ProfessionalChemist0.05.0-1.000.02.07-8 hoursModerateMDNo6.02.0No0
77RajveerMale38.0NashikWorking ProfessionalTeacher0.03.0-1.000.04.07-8 hoursUnhealthyB.PharmNo10.03.0Yes0
88AishwaryaFemale24.0BangaloreStudentStudent2.00.05.905.00.05-6 hoursModerateBScNo3.02.0Yes0
99SimranFemale42.0PatnaWorking ProfessionalElectrician0.04.0-1.000.01.05-6 hoursHealthyMEYes7.02.0Yes0
idNameGenderAgeCityWorking Professional or StudentProfessionAcademic PressureWork PressureCGPAStudy SatisfactionJob SatisfactionSleep DurationDietary HabitsDegreeHave you ever had suicidal thoughts ?Work/Study HoursFinancial StressFamily History of Mental IllnessDepression
140690140690RashiFemale18.0LudhianaStudentStudent5.00.06.882.00.0Less than 5 hoursHealthyClass 12Yes10.05.0No1
140691140691ZaraFemale57.0MeerutWorking ProfessionalTeacher0.01.0-1.000.01.0Less than 5 hoursModerateB.ArchYes4.05.0Yes0
140692140692RaunakMale49.0BhopalWorking ProfessionalFinancial Analyst0.04.0-1.000.01.07-8 hoursModerateMBANo9.01.0No0
140693140693ShauryaMale55.0SrinagarWorking ProfessionalData Scientist0.01.0-1.000.03.0Less than 5 hoursUnhealthyM.TechNo9.02.0No0
140694140694IshaaniFemale45.0AhmedabadWorking ProfessionalTeacher0.02.0-1.000.05.0Less than 5 hoursModerateB.EdYes1.05.0No0
140695140695VidyaFemale18.0AhmedabadWorking ProfessionalOther0.05.0-1.000.04.05-6 hoursUnhealthyClass 12No2.04.0Yes1
140696140696LataFemale41.0HyderabadWorking ProfessionalContent Writer0.05.0-1.000.04.07-8 hoursModerateB.TechYes6.05.0Yes0
140697140697AanchalFemale24.0KolkataWorking ProfessionalMarketing Manager0.03.0-1.000.01.0More than 8 hoursModerateB.ComNo4.04.0No0
140698140698PrachiFemale49.0SrinagarWorking ProfessionalPlumber0.05.0-1.000.02.05-6 hoursModerateMEYes10.01.0No0
140699140699SaiMale27.0PatnaStudentStudent4.00.09.241.00.0Less than 5 hoursHealthyBCAYes2.03.0Yes1